We already do this in tergm when running within-time-step MCMC.
We can keep track of the changes in Hamming distance between the initial network and the current MCMC state, a vector of +1s, 0s, and -1s. Initially, +1s will occur more frequently than -1s, as the network burns in. Eventually, once the network is sufficiently far away, they will balance out. At that point, we burn-in a bit longer to preserve the Markov property and conclude.
For valued ERGMs, we need some other notion of distance, perhaps sum of absolute differences.
Advantages:
A very simple approach.
Can be done entirely in C, so no need to go between R and C.
No need for time-series, Geweke, etc. diagnostics.
Pretty well tested in tergm.
Disadvantages:
May be unnecessarily conservative: typically we don't care if the network is burned-in but rather that the statistics are---and the latter have a much lower dimension than the former.
Requires storing the previous network.
Computational cost is incurred at every iteration from having to keep track.
We already do this in
tergmwhen running within-time-step MCMC.We can keep track of the changes in Hamming distance between the initial network and the current MCMC state, a vector of +1s, 0s, and -1s. Initially, +1s will occur more frequently than -1s, as the network burns in. Eventually, once the network is sufficiently far away, they will balance out. At that point, we burn-in a bit longer to preserve the Markov property and conclude.
For valued ERGMs, we need some other notion of distance, perhaps sum of absolute differences.
Advantages:
tergm.Disadvantages: